Dataset-based benchmarking
Upload real inputs and expected outputs, keep the dataset fixed across runs, and score every candidate on the same tasks.
Promptic
Promptic is a GenAI optimization platform for developers and teams building LLM applications. Benchmark prompts, models, and agent workflows.
GenAI evaluation and optimization
Promptic is a GenAI optimization platform for teams that need to evaluate and improve LLM prompts, models, and agent workflows. It uses a user's real inputs and expected outputs as a fixed evaluation dataset, then benchmarks candidates against selected KPIs such as quality, cost, and latency.
The platform combines OpenTelemetry-native tracing with automated optimization. Teams can review model and workflow behavior, identify where candidates succeed or fail, compare trade-offs, and ship a configuration that fits their stated priorities. Supported optimization areas described on the site include prompt tuning, model selection, agent architecture, information extraction, and tool use.
Core capabilities
KPI-driven evaluation
Set quality, cost, and latency objectives, then compare candidate configurations using the metrics relevant to the application.
Automated optimization
Automatically tune prompts, models, agent architectures, extraction workflows, and tool selection according to the supplied data and constraints.
OpenTelemetry tracing
Capture LLM calls, tool calls, and workflow steps through OpenTelemetry, including span-level token, cost, and latency information.
Trace-informed iteration
Inspect failing traces and compare alternatives before implementing a change, with results and evaluation evidence recorded for validation.
API and SDK access
The pricing page lists API/SDK access on Team, Business, and Enterprise plans, enabling teams to include Promptic in development and production workflows.
Practical use cases
Prompt and model selection
Compare prompt variants or foundation models on a representative dataset, then select the option that meets a team's quality target without overlooking cost and latency.
Agent architecture improvement
Evaluate router-based, tool-first, or single-agent designs against the same cases and inspect failing traces before changing the implementation.
Information extraction
Tune extraction workflows for structured outputs such as invoice fields by testing candidate configurations against expected results.
Tool-use optimization
Measure tool-selection behavior and investigate incorrect or repeated tool calls to improve how an agent chooses and uses tools.
Production workflow validation
Use trace data and recorded evaluation evidence as part of a development workflow, with API/SDK access available on applicable paid plans and enterprise deployment options for larger organizations.
Pros and Cons
Pros
- Uses a fixed dataset so candidate configurations can be compared on the same tasks.
- Evaluates quality, cost, and latency together instead of treating prompt quality as the only objective.
- Supports tracing for LLM calls, tool calls, and workflow steps through OpenTelemetry.
- Covers prompts as well as broader components such as models, agent architectures, extraction workflows, and tool selection.
- Offers API/SDK access on paid Team, Business, and Enterprise plans.
Cons
- The site provides limited public detail about setup, supported model providers, and the exact optimization methods used for different workflows.
- Pricing and retained-data allowances vary by plan, and managed model usage is billed separately from Promptic Credits.
FAQ
How does Promptic optimize an AI application?
Promptic starts with a fixed dataset of real inputs and expected outputs. Users select KPIs such as quality, cost, and latency, then benchmark candidates against the same data before reviewing findings and shipping a selected configuration.
What does Promptic tracing capture?
Promptic can capture LLM calls, tool calls, and workflow steps through OpenTelemetry. Traces include information such as tokens, cost, latency, model, and provider for recorded spans.
What parts of a GenAI workflow can Promptic optimize?
The platform can optimize prompts, model choices, agent architectures, information-extraction workflows, and tool selection against a user's data and KPIs. The exact optimization scope depends on the use case.
Which plans and deployment options are available?
The pricing page lists Free, Team, Business, and Enterprise plans. Team and Business add capabilities such as optimization, evaluations, tracing, and API/SDK access, while Enterprise supports custom terms and deployment options including Private Cloud or BYOC.
Can teams use their own model keys?
Yes. Promptic supports comparing candidate models and includes model access through either user-provided model keys or, on applicable paid plans, optional managed model usage billed separately.
Quick Facts
- Category
- AI developer tool
- Primary users
- GenAI and LLM application teams
- Evaluation inputs
- User-provided inputs and expected outputs
- Core KPIs
- Quality, cost, and latency
- Tracing standard
- OpenTelemetry
- Plans
- Free, Team, Business, and Enterprise
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